# -*- coding: utf-8 -*-
# Author   : ZhangQing
# Time     : 2025-08-14
# File     : llm_service.py
# Project  : codebuddy_craft
# Desc     : LLM服务

from typing import Dict, Any, List, Optional, AsyncGenerator
import json
import asyncio

class LLMService:
    """
    大语言模型服务
    
    处理与LLM的交互，包括聊天、代码生成等功能
    """
    
    def __init__(self):
        """初始化LLM服务"""
        self.model_config = {
            "temperature": 0.7,
            "max_tokens": 2000,
            "top_p": 0.95,
            "frequency_penalty": 0,
            "presence_penalty": 0
        }
    
    async def chat_completion(self, messages: List[Dict[str, Any]], **kwargs) -> Dict[str, Any]:
        """
        聊天完成
        
        发送消息到LLM并获取回复
        """
        # 模拟LLM响应
        await asyncio.sleep(1)
        return {
            "id": "chat-response-id",
            "object": "chat.completion",
            "created": 1692345678,
            "model": "codebuddy-model",
            "choices": [
                {
                    "index": 0,
                    "message": {
                        "role": "assistant",
                        "content": "这是一个模拟的LLM响应。在实际实现中，这里将连接到真实的LLM API。"
                    },
                    "finish_reason": "stop"
                }
            ],
            "usage": {
                "prompt_tokens": 100,
                "completion_tokens": 50,
                "total_tokens": 150
            }
        }
    
    async def streaming_chat_completion(self, messages: List[Dict[str, Any]], **kwargs) -> AsyncGenerator[str, None]:
        """
        流式聊天完成
        
        以流的形式获取LLM回复
        """
        # 模拟流式响应
        chunks = [
            "这是",
            "一个",
            "模拟的",
            "LLM",
            "流式",
            "响应。",
            "在实际实现中，",
            "这里将连接到",
            "真实的",
            "LLM API。"
        ]
        
        for chunk in chunks:
            await asyncio.sleep(0.2)
            yield json.dumps({
                "choices": [
                    {
                        "delta": {"content": chunk},
                        "finish_reason": None
                    }
                ]
            })
        
        # 发送结束标记
        yield json.dumps({
            "choices": [
                {
                    "delta": {},
                    "finish_reason": "stop"
                }
            ]
        })
    
    async def generate_code(self, prompt: str, language: str = "python") -> str:
        """
        生成代码
        
        根据提示生成指定语言的代码
        """
        # 模拟代码生成
        await asyncio.sleep(1)
        return f"# 这是模拟生成的{language}代码\ndef hello_world():\n    print('Hello, World!')"
    
    async def explain_code(self, code: str) -> str:
        """
        解释代码
        
        分析并解释提供的代码
        """
        # 模拟代码解释
        await asyncio.sleep(1)
        return "这段代码定义了一个简单的函数，用于打印'Hello, World!'。"